Novel Neural Interfaces For Upper-Limb Motor Rehabilitation After Stroke

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dc.contributor.advisor Birbaumer, Niels (Prof. Dr. Dr. hc. mult.)
dc.contributor.author Sarasola Sanz, Andrea
dc.date.accessioned 2019-06-24T07:16:53Z
dc.date.available 2019-06-24T07:16:53Z
dc.date.issued 2019-06-24
dc.identifier.other 1667818759 de_DE
dc.identifier.uri http://hdl.handle.net/10900/89800
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-898000 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-31181
dc.description.abstract Stroke is the third most common cause of death and the main cause of acquired adult disability in developed countries. The most common consequence of stroke is motor impairment, which becomes chronic in 56% of stroke survivors. However, reorganization of brain networks can occur in response to sensory input, expe- rience and learning. Although several post-stroke neurorehabilitation techniques have been investigated, there is no standardized therapy for severely impaired chronic stroke patients except for brain-machine interfaces (BMIs), which have shown positive results but still fail to elicit full motor function restoration. This work presents novel neural interfaces that aim to improve the existing rehabilita- tion therapies and to offer an alternative treatment to severely paralyzed stroke patients. First, we propose a novel myoelectric interface (MI) that is calibrated with electromyographic (EMG) data from the healthy limb, mirrored and used as a reference model for the paretic arm in order to reshape the pathological muscle synergy organization of stroke patients. A 4-session motor training with this mir- ror MI sufficed to induce motor learning in 10 healthy participants, suggesting that it might be a potential tool for the correction of maladaptive muscle activations and by extension, for the subsequent motor rehabilitation after stroke. Second, although significant positive results have been achieved with non-invasive BMIs based on electroencephalographic (EEG) activity, the functional motor recovery induced by such therapies still remains modest mainly due to poor decoding per- formance. Here, we explored the possibility of using novel algorithms to increase the performance of multi-class EEG-decoding of movements from the same limb, showing encouraging but still limited results. Finally, we propose integrating the novel mirror MI into a cortico-muscular hybrid BMI that combines brain and resid- ual muscle activity to increase decoding accuracy and hence, allow a more natural and dexterous control of the interface, facilitating neuroplasticity and motor re- covery. The system was validated in a healthy participant and a stroke patient, setting the premise for its application in a clinical setup. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-podok de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=en en
dc.subject.classification Elektromyographie , Elektroencephalographie , Rehabilitation , Gehirn-Computer-Schnittstelle , Schlaganfall de_DE
dc.subject.ddc 620 de_DE
dc.subject.ddc 610
dc.subject.other Chronic stroke en
dc.subject.other motor rehabilitation en
dc.subject.other neural interfaces en
dc.subject.other BMI en
dc.subject.other myoelectric interface en
dc.subject.other hybrid brain-machine interface en
dc.title Novel Neural Interfaces For Upper-Limb Motor Rehabilitation After Stroke en
dc.type Dissertation de_DE
dcterms.dateAccepted 2019-06-03
utue.publikation.fachbereich Medizin de_DE
utue.publikation.fakultaet 4 Medizinische Fakultät de_DE

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